A Robust Antenna Provides Tactile Feedback in a Multi-legged Robot
🤖 AI Summary
This work addresses the challenge of robust navigation for slender, multi-legged robots in narrow and complex environments, where frequent contacts and abrupt terrain changes hinder reliable operation. The authors propose a bioinspired tactile antenna with gradient compliance that enables real-time obstacle avoidance and locomotion regulation without relying on global maps or visual input. The system integrates a mechanically graded-stiffness structure, a continuous curvature–contact force model, and discrete collision-state inference to perceive local geometric features. A state-based motion controller is then employed to modulate gait and direction accordingly. Experimental results demonstrate that the robot can reliably steer and extricate itself in cluttered and confined spaces, significantly enhancing its autonomy and robustness in unstructured terrains.
Application Category
📝 Abstract
Multi-legged elongate robots hold promise for maneuvering through complex environments. Prior work has demonstrated that reliable locomotion can be achieved using open-loop body undulation and foot placement on rugose terrain. However, robust navigation through confined spaces remains challenging when body-environment contact is extensive and terrain rheology varies rapidly. To address this challenge, we develop a pair of tactile antennae for multi-legged robots that enable real-time sensing of surrounding geometry, modeling the morphology and function of biological centipede antennae. Each antenna features gradient compliance, with a stiff base and soft tip, allowing repeated deformation and elastic recovery. Robophysical experiments reveal a relationship between continuous antenna curvature and contact force, leading to a simplified mapping from antenna deformation to inferred discrete collision states. We incorporate this mapping into a controller that selects among a set of locomotor maneuvers based on the inferred collision state. Experiments in obstacle-rich and confined environments demonstrate that tactile feedback enables reliable steering and allows the robot to recover from near-stuck conditions without requiring global environmental information or real-time vision. These results highlight how mechanically tuned tactile appendages can simplify sensing and enhance autonomy in elongate multi-legged robots operating in constrained spaces.
Problem
Research questions and friction points this paper is trying to address.
multi-legged robot
tactile feedback
confined environments
robust navigation
terrain rheology
Innovation
Methods, ideas, or system contributions that make the work stand out.
tactile antenna
gradient compliance
multi-legged robot
robophysical modeling
collision state inference
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